Examining Tongue Movement Intentions in EEG-Based BCI with Machine and Deep Learning: An Approach for Dysphagia Rehabilitation

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dc.contributor.authorAslan, Sevgi Gokce
dc.contributor.authorYilmaz, Bulent
dc.date.accessioned2026-04-04T13:31:23Z
dc.date.available2026-04-04T13:31:23Z
dc.date.issued2024
dc.departmentİnönü Üniversitesi
dc.description.abstractDysphagia, a common swallowing disorder particularly prevalent among older adults and often associated with neurological conditions, significantly affects individuals' quality of life by negatively impacting their eating habits, physical health, and social interactions. This study investigates the potential of brain-computer interface (BCI) technologies in dysphagia rehabilitation, focusing specifically on motor imagery paradigms based on EEG signals and integration with machine learning and deep learning methods for tongue movement. Traditional machine learning classifiers, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, AdaBoost, Bagging, and Kernel were employed in discrimination of rest and imagination phases of EEG signals obtained from 30 healthy subjects. Scalogram images obtained using continuous wavelet transform of EEG signals corresponding to the rest and imagination phases of the experiment were used as the input images to the CNN architecture. As a result, KNN (79.4%) and SVM (63.4%) exhibited lower accuracy rates compared to ensemble methods like AdaBoost, Bagging, and Random Forest, all achieving high accuracy rates of 99.8%. These ensemble techniques proved to be highly effective in handling complex EEG datasets, particularly in distinguishing between rest and imagination phases. Furthermore, the deep learning approach, utilizing CNN and Continuous Wavelet Transform (CWT), achieved an accuracy of 83%, highlighting its potential in analyzing motor imagery data. Overall, this study demonstrates the promising role of BCI technologies and advanced machine learning techniques, especially ensemble and deep learning methods, in improving outcomes for dysphagia rehabilitation.
dc.identifier.doi10.2478/ebtj-2024-0017
dc.identifier.endpage183
dc.identifier.issn2564-615X
dc.identifier.issue4
dc.identifier.orcid0000-0001-9425-1916
dc.identifier.orcid0000-0003-2954-1217
dc.identifier.scopus2-s2.0-85208924854
dc.identifier.scopusqualityQ3
dc.identifier.startpage176
dc.identifier.urihttps://doi.org/10.2478/ebtj-2024-0017
dc.identifier.urihttps://hdl.handle.net/11616/108775
dc.identifier.volume8
dc.identifier.wosWOS:001335908300004
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSciendo
dc.relation.ispartofEurobiotech Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectBCI
dc.subjectdysphagia
dc.subjectCNN
dc.subjectmachine learning
dc.subjectEEG
dc.subjectmotor imagery
dc.titleExamining Tongue Movement Intentions in EEG-Based BCI with Machine and Deep Learning: An Approach for Dysphagia Rehabilitation
dc.typeArticle

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